The Model T Meets Development Tech: Changing the business model for global health data collection

When Henry Ford released his first mass-produced car, the iconic Model T, in 1908 very few people owned an automobile. They were hand-built by expensive craftsmen and required a full-time mechanic to keep them running. Not surprisingly, they were seen as a luxury item for the rich.

Ford did not invent the automobile. What he invented was a means of production – the assembly line, using standardized parts – that dramatically lowered the costs both of producing the vehicles and maintaining them. With a lower production cost per car, Ford was free to adopt a new business model, as well: Lower the price and make it up in volume.

Ford realized that the potential market for cheap and easy-to-use cars was much larger than the market for expensive and hard-to-maintain cars.

We’ve seen similar market approaches countless times since, when new technologies allow us to lower the cost of producing some useful tool. The price plummets and the market expands dramatically – and new business models become possible.

Development tech: No Ford and no data

Given all this history, it’s a wonder we haven’t applied these approaches to international development. In the development sector, tech tools are expensively hand-crafted by programmers and laboriously and expensively maintained by specialists. Since this limits the market to the few organizations that can afford to pay for these tools, international development and global health still don’t have the data needed to operate efficiently.

Take the Millennium Development Goals (MDGs). For many or most of them (child mortality in poor countries, for example), we never had really reliable data to determine if we’ve met the goals. In 2005 (five years after they were introduced), a University of Ottawa paper stated: “Many of the most important MDGs suffer from a worrying lack of scientifically valid data. … Often … one cannot know the baseline condition before the MDGs, or know if the desired trend of improvement is actually occurring.”

Applying Ford, Bezos and Zuckerberg

With this problem of data in development, the goal of Magpi is to create a business model that radically drops the cost of collecting more data. And we do that exactly as Henry Ford would have done: mass production and standardization.

In 2003, Magpi (under the name EpiSurveyor) started out as one of the first web-based applications aimed at global health. Today, Magpi is a leading provider of configurable, cloud-based mobile data collection and communication applications. It enables people in the ?eld to quickly and easily collect data on any mobile device.

Why did we move from being an installed-on-your-laptop, Windows-based program to a cloud app for development? For the same reason that Google Maps is cloud-based: It’s way cheaper.

Why did we design the system so that most people don’t need any formal training?

For the same reason Facebook does: Because it’s way cheaper.

Why did we eliminate the need for programmers and consultants to handcraft each data collection system?

For the same reason Skype does: Because – wait for it – it’s way cheaper.

We know from the experience of Ford and Zuckerberg, and Bezos and Page – and Ikea Founder Ingvar Kamprad, for that matter – that dropping prices grows markets. It brings enormous numbers of potential customers – who never imagined they could be potential customers – into the fold.

But does it work?

Of course, there are big differences between commercial markets and development agencies, and between consumer apps and business apps. But in just three years, Magpi has gone from 3,000 registered users to 33,000.

Probably most interesting is that our approach – web-based service, in both free and paid versions, user-customizable – moved way beyond the health data collection we originally envisioned. Six years after launching our online system for global health as a grant-supported organization, we now have people using Magpi for global health, for conservation and the environment, for labor rights, for research of all kinds, and also for commercial use.

It turns out that the need to collect form-based data quickly and cheaply isn’t limited to development or health organizations. Just think of how many people you’ve seen in your life filling out paper forms on a clipboard: political pollsters, hard-hat-wearing construction inspectors, the U.S. census and survey-takers at the shopping mall, just to name a few examples.

So when we think about the market size for Magpi, we think about all the millions and millions of people who need to collect data for one purpose or another – and how many will be collecting data as we lower the costs of doing it even more.